AgentSkillsCN

kn-implement

在执行某项任务时使用——遵循计划、检查ACs、追踪进度

SKILL.md
--- frontmatter
name: kn-implement
description: Use when implementing a task - follow the plan, check ACs, track progress

Implementing a Task

Execute the implementation plan, track progress, and complete the task.

Announce: "Using kn-implement for task [ID]."

Core principle: CHECK AC ONLY AFTER WORK IS DONE.

Step 1: Review Task

json
mcp__knowns__get_task({ "taskId": "$ARGUMENTS" })

If task status is "done" (reopening):

json
mcp__knowns__update_task({
  "taskId": "$ARGUMENTS",
  "status": "in-progress",
  "appendNotes": "Reopened: <reason>"
})
mcp__knowns__start_time({ "taskId": "$ARGUMENTS" })

Verify: plan exists, timer running, which ACs pending.

Step 2: Check Templates

json
mcp__knowns__list_templates({})

If template exists → use it to generate boilerplate.

Step 3: Work Through Plan

For each step:

  1. Do the work
  2. Check AC (only after done!)
  3. Append note
json
mcp__knowns__update_task({
  "taskId": "$ARGUMENTS",
  "checkAc": [1],
  "appendNotes": "Done: brief description"
})

Step 4: Handle Scope Changes

Small: Add AC + note

json
mcp__knowns__update_task({
  "taskId": "$ARGUMENTS",
  "addAc": ["New requirement"],
  "appendNotes": "Scope: added per user"
})

Large: Stop and ask user.

Step 5: Validate & Complete

  1. Run tests/lint/build
  2. Validate to catch broken refs:
json
mcp__knowns__validate({})
  1. Add implementation notes (use appendNotes, NOT notes!)
  2. Stop timer + mark done
json
mcp__knowns__stop_time({ "taskId": "$ARGUMENTS" })
mcp__knowns__update_task({
  "taskId": "$ARGUMENTS",
  "status": "done"
})

Step 6: Extract Knowledge (optional)

If patterns discovered: /kn-extract $ARGUMENTS

Checklist

  • All ACs checked
  • Tests pass
  • Validated (no broken refs)
  • Notes added
  • Timer stopped
  • Status = done

Red Flags

  • Checking AC before work done
  • Skipping tests
  • Skipping validation
  • Using notes instead of appendNotes
  • Marking done without verification